Skip to Main Content
IBM Data and AI Ideas Portal for Customers

Shape the future of IBM!

We invite you to shape the future of IBM, including product roadmaps, by submitting ideas that matter to you the most. Here's how it works:

Post your ideas

Post ideas and requests to enhance a product or service. Take a look at ideas others have posted and upvote them if they matter to you,

  1. Post an idea

  2. Upvote ideas that matter most to you

  3. Get feedback from the IBM team to refine your idea

Help IBM prioritize your ideas and requests

The IBM team may need your help to refine the ideas so they may ask for more information or feedback. The product management team will then decide if they can begin working on your idea. If they can start during the next development cycle, they will put the idea on the priority list. Each team at IBM works on a different schedule, where some ideas can be implemented right away, others may be placed on a different schedule.

Receive notification on the decision

Some ideas can be implemented at IBM, while others may not fit within the development plans for the product. In either case, the team will let you know as soon as possible. In some cases, we may be able to find alternatives for ideas which cannot be implemented in a reasonable time.

Additional Information

To view our roadmaps:

Reminder: This is not the place to submit defects or support needs, please use normal support channel for these cases

IBM Employees:

The correct URL for entering your ideas is:

Status Future consideration
Created by Deleted user
Created on Jan 9, 2018

Make it possible to explicitly kick off the ranker training process

Currently the ranker model building kicks off some indeterminate amount of time after training samples are added to WDS.  Would like the ability to:

  1. add a batch of training data
  2. trigger the training process
  3. poll the collection details api endpoint until model has finished building
  4. run a set of queries and collect result relevancy metrics
  5. add more training data
  6. poll until training is done
  7. re-run test queries and collect result relevancy metrics
  8. ... repeat until all the training batches are added...
  9. make plots of (improved) relevancy performance on the test set as training set size is increased

Right now -- after step 5, I poll the api and have to wait a really really long time.  There are two separate delays
(1) delay while waiting for the model training process to begin (no idea what schedules this, it seems like maybe its time based)

(2) delay while waiting for training to complete.  


The second delay is usually much less than the first for my datasets.  If i could trigger the training process I could eliminate the first delay entirely.